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Free keywords:
Computer Science, Human-Computer Interaction, cs.HC
Abstract:
As first-person cameras in head-mounted displays become increasingly
prevalent, so does the problem of infringing user and bystander privacy. To
address this challenge, we present PrivacEye, a proof-of-concept system that
detects privacysensitive everyday situations and automatically enables and
disables the first-person camera using a mechanical shutter. To close the
shutter, PrivacEye detects sensitive situations from first-person camera videos
using an end-to-end deep-learning model. To open the shutter without visual
input, PrivacEye uses a separate, smaller eye camera to detect changes in
users' eye movements to gauge changes in the "privacy level" of the current
situation. We evaluate PrivacEye on a dataset of first-person videos recorded
in the daily life of 17 participants that they annotated with privacy
sensitivity levels. We discuss the strengths and weaknesses of our
proof-of-concept system based on a quantitative technical evaluation as well as
qualitative insights from semi-structured interviews.
